In 2050, it is expected that 70% of the world’s population will live in cities (Jin et al., 2014), leading to increasing congestion in and surrounding cities. This will raise new challenges, requiring more efficient and interactive cities. A novel paradigm contributing to these s
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In 2050, it is expected that 70% of the world’s population will live in cities (Jin et al., 2014), leading to increasing congestion in and surrounding cities. This will raise new challenges, requiring more efficient and interactive cities. A novel paradigm contributing to these so-called smart cities is participatory sensing. Also known as mobile crowdsourcing, this solution enables both public and professional users to actively gather, analyse, and share local data about the urban environment using built-in sensors in smart devices (Truong et al., 2019). Considering that over 94% of the population has access to a mobile network, obtaining real-time data from these already existing sensors can be a low-cost solution for acquiring a huge amount of information (International Telecommunication Union, 2016). These real-time data can be used to analyse and predict mobility flows, and make public and private transport more efficient, safe, and sustainable.
However, a clear benefit is required to motivate smart device users to share data about their activities and their environment. Sharing data comes with the risk of disclosing private information, as location data can lead to the identification of living and work locations, as well as individual habits. Research on motivations of
smart device users to engage in participatory sensing tasks is required in order to be able to design valuesensitive participatory sensing applications. This study aims to identify factors related to incentives and privacy that explain choice behaviour of users in participatory sensing applications. The main research
question being addressed is as follows: “How do factors relating to incentives and privacy affect the willingness among smart device users to contribute to participatory sensing systems for smart mobility?”
A choice modelling approach was taken in order to identify the trade-offs made by users between potential benefits and costs of sharing data. This is an approach not often used before in the field of participatory sensing and provides novel insights in user behaviour in these systems. First, a literature review was
conducted identifying possible factors relating to incentives and privacy, influencing the willingness of people to share data. Five factors were selected: monetary reward, effort, risk of re-identification, types of data, and
data use. These factors were incorporated in a stated choice experiment distributed among smart device users through an online survey. In total, 125 valid responses were collected. The required effort of participating was regarded the most important factor influencing the willingness to share data in sensing
applications for smart mobility. This provides new insights, as previous studies do not include effort in choice experiments regarding data sharing. As expected, the perceived ease of use declines if more inputs by the user are required. Moreover, respondents are reluctant to the collection of contextual and multimedia in
addition to location and motion data, a finding which is confirmed by recent studies. Almost half of the respondents indicated to be highly concerned about their privacy. Therefore, a surprising finding is that the risk of re-identification was regarded the least important factor influencing the willingness to share data.
However, when taking a deeper look at the data, it appears there is a group of people having extreme preferences regarding privacy and trust, who assign a higher importance to privacy related factors (risk of re-identification, types of data, data use).
The identified trade-offs were used to evaluate the implications for different use cases in the field of smart mobility. Three interviews were conducted, which each led to the definition of a use case, being crowd management in a city, safety research using car accident information, and real-time travel information in
public transport. By aggregating the quantitative and qualitative parts of the research, it can be concluded that the accuracy of collected information can be improved by collecting more types of data in addition to location data. However, this will lead to a decline in the acceptance rate. A proposed solution is to provide
tailor-made sensing applications, giving the user control to indicate which data they agree to share.
Furthermore, the communication of the purpose of the data collection is important to users. Moreover, being transparent about the risks related to the data collection can help users to make a well-informed decision and
will ensure an ethical design of sensing applications. Finally, increasing the attractiveness of the application is recommended to reduce the perceived effort, which could be done by gamification of sensing tasks.
Besides societal implications, this study provides several recommendations for future research. First, it is recommended to repeat the experiment without the provision of a financial compensation, in order to see if this leads to different choice behaviour and preferences of users. Furthermore, research on the understanding of privacy risks among users is recommended. Finally, this study can be used outside the smart mobility field, in order to analyse the willingness to share data in a broader sense.
